HiRAG and RAG-ARC
About HiRAG
hhy-huang/HiRAG
[EMNLP'25 findings] This is the official repo for the paper, HiRAG: Retrieval-Augmented Generation with Hierarchical Knowledge.
This project helps you get more accurate and comprehensive answers from large language models (LLMs) when querying your specific documents or knowledge base. You provide your textual content, and it processes it to enable a system that understands the hierarchical relationships within your information. The result is a more insightful and detailed response to your questions. This is for data scientists, researchers, or anyone building advanced question-answering systems over their proprietary data.
About RAG-ARC
DataArcTech/RAG-ARC
A modular, high-performance Retrieval-Augmented Generation framework with multi-path retrieval, graph extraction, and fusion ranking
This project helps professionals working with large volumes of documents (like PDFs, PowerPoints, and Excel files) to extract precise answers and generate content. It takes your unstructured documents and questions, then processes them to provide accurate, context-rich responses or summarized information. Knowledge managers, researchers, and content creators who need to quickly retrieve and synthesize information from extensive knowledge bases would find this invaluable.
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